The typical retail organization uses a reorder point system to manage inventories at the store-level. These are execution systems rather than planning systems. They review products and if the on-hand balance is below a preset number (the reorder point), an order is created to replenish inventory. If the on-hand balance is above the reorder point, no further action is taken. No projections are made into the future for the purposes of planning product requirements from the suppliers or manufacturers. No projections are made for the purposes of planning projected inventory levels. These are strictly inventory management systems designed to release orders at the appropriate time.
Manufacturing companies have used time-phased planning systems (also called DRP or Distribution Resource Planning or Distribution Requirements Planning) beginning with implementations in 1975. These systems provide projections into the future which can be used to plan product requirements, transportation requirements, capacity requirements and financial requirements. However, these systems are designed to meet the needs of a manufacturing organization, and have not proved suitable for a retailer's needs. These systems are not able to process the large data volumes typical of most retail organizations. Additionally, manufacturing systems are not designed to deal with certain needs that are specific to retailers.
A typical manufacturing organization of the type that would supply retail stores might stock several hundred to a thousand products at one to ten different suppliers. This results in ten thousand stock keeping locations. A typical retailer might stock ten thousand to fifty thousand products in one to several thousand locations. This results in as many as 100 million stock keeping locations. Systems designed for tens of thousands of stock keeping locations could theoretically be made to work for a hundred million stock keeping locations, but as a result of limitations in computing resources and time available to complete the computing operations this has not been achieved in actual practice. There are numerous technical hurdles to overcome in order to process the amount of data for a retail store supply chain during the relatively short time window in which the processing needs to be done when the system is operated in batch processing mode. Most retailers get their sales history (or point of sale data or POS) late in the evening or in the early hours of the morning. Several hours later, the replenishment planning must be complete in order to load trucks for deliveries. Additionally, using systems not designed to economically process this amount of data would force the retailer to purchase significantly more computer processing power than would be needed with a system designed for these volumes (assuming such a system existed).
Many retail stores stock a significant number of products that would be categorized as low volume or slow-moving products. Rather than selling thousands of a particular product at a particular store during a year, the retail store might sell only 5 or 20 of the product in a year. While any one of these low-volume products do not make up a large percentage of a retailer's shipments, in total, low volume products do constitute a significant percentage of a retailer's shipments. Therefore, any time-phased planning system for retail must include a way to deal with these low-volume products.
Manufacturing companies also stock low-volume products. However, the percentage of a manufacturer's shipments that would be considered low volume is typically much less than the percentage of a retailer's shipments that would be considered low volume. For this reason, current time-phased planning systems do not provide the same capabilities for low-volume products.
Known time-phased planning logic for calculating planned replenishment shipments for low-volume products typically gives an inaccurate picture of total demand for the product, of total demand for transportation planning, of total demand for capacity planning, and of total demand for financial planning. Such known logic subtracts the forecast from the projected on-hand balance to give the new projected on-hand balance. If the new projected on-hand balance is below the safety stock, a planned replenishment shipment is either created, or an existing planned replenishment shipment is automatically rescheduled to the need date. In this context, rescheduling means changing the receipt date from whatever value currently exists to the date of the forecast which caused the projected on-hand balance to drop below the safety stock. In addition, rescheduling means changing the ship date of the planned replenishment shipment to the receipt date less the lead time.
To illustrate how known logic typically handles low-volume products, assume a low-volume product sells such that the daily forecast is 0.01 unit per day for the first day, and the on-hand balance in the store is 2, and the safety stock is 2. The projected on-hand balance calculation for the first day would give a new projected on-hand balance of 1.99. Since 1.99 is less than the safety stock of 2, a planned order would be created for immediate shipment to the store.
This is not what most retailers would want done. The forecast of 0.01 unit is so small that it is not appropriate to send one product to the store in anticipation of this sale. Most retailers would rather wait until a sale happens and the product is below the safety stock before shipping a product to the store. For example, as long as the on-hand balance in the store equals the safety stock (2 in this example), no planned shipments would be made to the store.
An additional consequence of the way in which known logic handles low-volume products is that a large number of planned replenishment shipments for such products will tend to “bunch up” or accumulate in the first few time periods. For example, several thousand planned replenishment shipments for low-volume products may exist in the first few days, and then the number of planned replenishment shipments for low-volume products would drop to either zero or a very small number. However, this “bunching up” does not represent a valid simulation of what is likely to happen. Instead of several thousand shipments for low-volume products happening in the first few days and then no shipments happening after that, there will typically be some shipments for low-volume products each day out into the future. For this reason, the traditional logic of time-phased replenishment planning does not handle low-volume products well.
In addition to planning for product replenishment, retailers also need to plan for transportation and capacity. Transportation is the weight and cube (i.e., three-dimensional volume) of merchandise that needs to be transported from one location to another. Capacity is the manpower or equipment needed to pick, pack, receive, and store the merchandise either in the distribution centers or the stores.
Traditional systems for retail have tended to be implemented as separate systems by function, such as a replenishment system, a transportation planning system, a capacity planning system, and so on. As a result, these systems tended to have different numbers for given products at a specific point in time. For example, the replenishment system may have a large shipment scheduled to be received into the distribution center in ten weeks, yet the transportation planning system, which might have been using the history of shipments from last year, did not show this order. As a result, several sets of numbers existed in these systems, as opposed to a single set of numbers that are used by all systems. Consequently, when people in one department attempt to work with people in other departments, a good deal of unproductive time is often spent reconciling the numbers in one system to the numbers in one of the other systems.